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BitNet: Scaling 1-bit Transformers for Large Language Models
Paper • 2310.11453 • Published • 105 -
Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection
Paper • 2310.11511 • Published • 78 -
In-Context Learning Creates Task Vectors
Paper • 2310.15916 • Published • 43 -
Matryoshka Diffusion Models
Paper • 2310.15111 • Published • 44
Collections
Discover the best community collections!
Collections including paper arxiv:2311.08263
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When can transformers reason with abstract symbols?
Paper • 2310.09753 • Published • 4 -
In-Context Pretraining: Language Modeling Beyond Document Boundaries
Paper • 2310.10638 • Published • 30 -
Reward-Augmented Decoding: Efficient Controlled Text Generation With a Unidirectional Reward Model
Paper • 2310.09520 • Published • 12 -
Connecting Large Language Models with Evolutionary Algorithms Yields Powerful Prompt Optimizers
Paper • 2309.08532 • Published • 53
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BitNet: Scaling 1-bit Transformers for Large Language Models
Paper • 2310.11453 • Published • 105 -
Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection
Paper • 2310.11511 • Published • 78 -
In-Context Learning Creates Task Vectors
Paper • 2310.15916 • Published • 43 -
Matryoshka Diffusion Models
Paper • 2310.15111 • Published • 44
-
When can transformers reason with abstract symbols?
Paper • 2310.09753 • Published • 4 -
In-Context Pretraining: Language Modeling Beyond Document Boundaries
Paper • 2310.10638 • Published • 30 -
Reward-Augmented Decoding: Efficient Controlled Text Generation With a Unidirectional Reward Model
Paper • 2310.09520 • Published • 12 -
Connecting Large Language Models with Evolutionary Algorithms Yields Powerful Prompt Optimizers
Paper • 2309.08532 • Published • 53